

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
    import torch





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "char"
   lowerCamelCase__				=      "bpe"
   lowerCamelCase__				=      "wp"




__A			:					Optional[Any]						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      ["image_processor", "char_tokenizer"]
   lowerCamelCase__				=      "ViTImageProcessor"
   lowerCamelCase__				=      "MgpstrTokenizer"





   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			Dict=None     , __lowerCamelCase					:			Union[str, Any]=None     , **__lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."     , __lowerCamelCase     , )
           SCREAMING_SNAKE_CASE										=   kwargs.pop("feature_extractor"			)

       SCREAMING_SNAKE_CASE										=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."			)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."			)

       SCREAMING_SNAKE_CASE										=   tokenizer
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("gpt2"			)
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("bert-base-uncased"			)

       super().__init__(__lowerCamelCase     , __lowerCamelCase			)





   def __call__(       self					:			int     , __lowerCamelCase					:			Optional[int]=None     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			Any=None     , **__lowerCamelCase					:			str			):
       if images is None and text is None:
           raise ValueError("You need to specify either an `images` or `text` input to process."			)

       if images is not None:
           SCREAMING_SNAKE_CASE										=   self.image_processor(__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase			)
       if text is not None:
           SCREAMING_SNAKE_CASE										=   self.char_tokenizer(__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase			)

       if text is None:
           return inputs
       elif images is None:
           return encodings
       else:
           SCREAMING_SNAKE_CASE										=   encodings["input_ids"]
           return inputs





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   sequences
       SCREAMING_SNAKE_CASE										=   char_preds.size(0			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "char"			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "bpe"			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "wp"			)

       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   []
       for i in range(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   [char_scores[i], bpe_scores[i], wp_scores[i]]
           SCREAMING_SNAKE_CASE										=   [char_strs[i], bpe_strs[i], wp_strs[i]]
           SCREAMING_SNAKE_CASE										=   scores.index(max(__lowerCamelCase			)			)
           final_strs.append(strs[max_score_index]			)
           final_scores.append(scores[max_score_index]			)

       SCREAMING_SNAKE_CASE										=   {}
       SCREAMING_SNAKE_CASE										=   final_strs
       SCREAMING_SNAKE_CASE										=   final_scores
       SCREAMING_SNAKE_CASE										=   char_strs
       SCREAMING_SNAKE_CASE										=   bpe_strs
       SCREAMING_SNAKE_CASE										=   wp_strs
       return out





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[str]			):
       if format == DecodeType.CHARACTER:
           SCREAMING_SNAKE_CASE										=   self.char_decode
           SCREAMING_SNAKE_CASE										=   1
           SCREAMING_SNAKE_CASE										=   "[s]"
       elif format == DecodeType.BPE:
           SCREAMING_SNAKE_CASE										=   self.bpe_decode
           SCREAMING_SNAKE_CASE										=   2
           SCREAMING_SNAKE_CASE										=   "#"
       elif format == DecodeType.WORDPIECE:
           SCREAMING_SNAKE_CASE										=   self.wp_decode
           SCREAMING_SNAKE_CASE										=   102
           SCREAMING_SNAKE_CASE										=   "[SEP]"
       else:
           raise ValueError(f"Format {format} is not supported."			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   [], []
       SCREAMING_SNAKE_CASE										=   pred_logits.size(0			)
       SCREAMING_SNAKE_CASE										=   pred_logits.size(1			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   pred_logits.topk(1     , dim=-1     , largest=__lowerCamelCase     , sorted=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   preds_index.view(-1     , __lowerCamelCase			)[:, 1:]
       SCREAMING_SNAKE_CASE										=   decoder(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.nn.functional.softmax(__lowerCamelCase     , dim=2			).max(dim=2			)
       SCREAMING_SNAKE_CASE										=   preds_max_prob[:, 1:]

       for index in range(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   preds_str[index].find(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   preds_str[index][:pred_eos]
           SCREAMING_SNAKE_CASE										=   preds_index[index].cpu().tolist()
           SCREAMING_SNAKE_CASE										=   pred_index.index(__lowerCamelCase			) if eos_token in pred_index else -1
           SCREAMING_SNAKE_CASE										=   preds_max_prob[index][: pred_eos_index + 1]
           SCREAMING_SNAKE_CASE										=   pred_max_prob.cumprod(dim=0			)[-1] if pred_max_prob.nelement() != 0 else 0.0
           dec_strs.append(__lowerCamelCase			)
           conf_scores.append(__lowerCamelCase			)

       return dec_strs, conf_scores





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   [seq.replace(" "     , ""			) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase			)]
       return decode_strs





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]			):
       return self.bpe_tokenizer.batch_decode(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   [seq.replace(" "     , ""			) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase			)]
       return decode_strs

"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()